The Effectiveness of Credit in Poverty Elimination: an Application to
Rural Vietnam 1
Carol Newman
Department of Economics, Trinity College Dublin
Finn Tarp
Department of Economics, University of Copenhagen
1. Introduction
In low income countries, the economic choices of poor households are often
constrained by the inefficient operation of local financial markets (Banerjee and
Duflo, 2007). A key issue for developing countries is the extent to which households
can access financial products, particularly in the formal sector. In particular, providing
access to borrowings that can be put to productive uses has the potential to lead to
long term economic growth. Bencivenga and Smith (1993), among others, explore the
effect of a reduction in credit rationing on capital accumulation and economic growth.
Bose and Cothren (1997) extend this notion further to illustrate that in response to the
accumulation of capital, credit will become less rationed, which will in turn lead to
further capital accumulation as investment loans become more available. Through this
channel, government policies aimed at facilitating access to credit can lead to growth
and economic development. The type of credit available, however, will also matter.
Modigliani (1986) and Japelli and Pagano (1994) find that the rationing of
consumption loans may have a positive effect on development. If consumption loans
are not available, households are more likely to save in order to inter-temporarily
smooth consumption and will only borrow for investment purposes which will lead to
an accumulation of capital. Hung (2005) suggests, therefore, that in some cases a
government policy of financial repression, where credit for consumption purposes is
rationed, may have positive effects on economic development.
Policies aimed at alleviating poverty, however, should also consider their impact on
the very poor. Given that the probability of default is generally negatively correlated
with income and wealth, many formal financial institutions may be reluctant to lend to
the poor. As such we might expect to find that poor households rely to a greater extent
on informal credit sources than non-poor households. If the effectiveness of informal
credit in improving outcomes is less than that of credit accessed through formal
sources then this may have implications for the role credit can play in alleviating
poverty. Aubert et al. (2009) discuss the importance of creating the right incentives
for credit agents in financial institutions to acquire information on potential borrowers
so that they are selected in accordance with pro-poor policy objectives.2
1
This paper constitutes an in-depth study written under two Danida Viet Nam programs, namely the
Business Sector Program Support (BSPS) and the Agricultural and Rural Development Sector Program
Support (ARD-SPS) using data collected by the Viet Nam Access to Resources Household Surveys
(VARHSs) of 2006 and 2008. We are grateful for very helpful comments and suggestions from Ms Le
Xuan Quynh (CIEM) and Ms Pham Thi Lien Phuong (CAP/IPSARD), as well as seminar participants
at a workshop held on 11th March 2010 at the Central Institute for Economic Management (CIEM).
2
Auber et al. (2009) refer in particular to microfinance institutions but their findings can be generalized
to other formal financial institutions.
1
Finally, any discussion of the role and effectiveness of credit should also consider its
interaction with other financial markets. The availability of insurance, for example,
may help households better manage their exposure to risks thus freeing up credit for
productive uses. In the absence of insurance credit may be used instead to buffer
against unexpected income losses.3 The availability of formal savings facilities may
also have an effect. Ahlin and Jiang (2008) explore the long-run effects of microcredit on development. They find that the extent to which the availability of microcredit can have persistent positive effects on growth and development depends on the
extent to which it facilitates the ‘graduation’ of the self-employed from small scale to
large scale production. For positive long-run effects to be realized the self-employed
must also have the ability to save the returns to self-employment in order to
accumulate wealth.
In Vietnam, formal credit is provided to households in rural areas through two main
state-owned banks, the Vietnamese Bank for Social Policies (VBSP) and the
Vietnamese Bank for Agriculture and Rural Development (VBARD). The market is
also serviced by a small number of other state-owned and private commercial banks.
The VBSP behaves much like a social policy tool with a structured lending
programme offering low interest credit for targeted categories of households including
the poor, the disadvantaged and the disabled. In contrast the VBARD operates on a
commercial basis. Households also rely to a large on informal credit available through
Rotating Credit and Savings Associations (ROSCAs), socio-political groups such as
Women’s Unions and Farmer’s Unions, and borrowing from friends and relative. The
literature suggests that the extent to which the availability of credit will lead to
improvements in outcomes and alleviate poverty will depend on: 1) the purpose of the
loans obtained; 2) whether they are sourced formally or informally; and 3) the
interaction of credit markets with other financial markets such as savings and
insurance. In this paper, we attempt to ascertain the extent to which credit markets in
Vietnam, across each of these domains, impact on poverty reducing outcomes
focusing, in particular, on the role of formal and informal sources of credit and the
VBSP and the VBARD in this process. We perform an in-depth analysis of the
workings of credit markets in rural Vietnam for the period 2006 and 2008. First, we
construct a profile of the types of households that borrow from different sources
(formal and informal). Second, we consider whether differences exist in the
characteristics of households that borrow for different purposes (investment,
consumption etc). We base both analyses on the 2006 Vietnamese Access to
Resources Household Survey (VARHS) data. The third part of the empirical
investigation analyses the effectiveness of credit in improving outcomes for
households who borrow. In order to achieve this, we exploit the panel structure of the
2006 and 2008 VARHS in considering how past loans affect changes in a variety of
outcomes that determine welfare such as income, diversification, investment and
productivity. We pay close attention to the source of the loans obtained and their
purpose. Ultimately, we aim to use our results to help better inform policy in relation
to the future development of rural credit markets in Vietnam. It should be noted,
however, that this paper does not address the extent to which extending credit is the
best approach to alleviating poverty given that other policy instruments are not
3
Giné and Yang (2009), however, find no evidence of a link between willingness to use loans for
technology adoption with the availability of formal insurance using a randomized field experiment in
Malawi. They found that households already had other informal risk sharing mechanisms in place in
the event of loan default.
2
considered. Rather, this paper presents evidence on how effective credit, accessed
through different sources and for different purposes, is on various welfare outcomes.
The paper is structured as follows. The data are presented in Section 2. Section 3
presents the empirical results from both stages of the analysis. Section 4 provides a
summary and policy recommendations.4 We conclude with a discussion of future
work.
2. Data
The data are taken from the Vietnam Access to Resources Household Survey
(VARHS) implemented in 2006 and 2008 in 12 provinces in Vietnam.5 The
households for which a full representative panel is available are spread over 437
communes, 130 districts and total 1,200 households. Along with detailed demographic
information on household members, the survey includes sections on financial
behavior, in particular in relation to borrowing and savings.
Table 1 presents descriptive statistics on the nature and types of loans obtained by
households in our sample in 2006.6 Sixty-six per cent of households have one or more
loans in 2006. There is a lot of variability across provinces with 44 per cent of
households having a loan in Ha Tay compared with 91 per cent in Lam Dong. This
suggests that either access to credit varies across provinces or the needs of
households, in terms of credit requirements, differs across provinces. We also
compare households by wealth where quintiles are constructed using principal
component analysis of the characteristics of the household dwelling place, such as the
size and value of the dwelling, energy supply, sanitation facilities and water supply
and ownership of durable goods. A greater proportion of households in the lowest
wealth quintile (75 per cent) have a loan with loans valuing over 100 per cent of
annual income (compared with 77 per cent on average). This suggests that households
in this quintile may be over-indebted. This is further validated by the statistics for
poor compared with non-poor households, where poor households are defined as
those households in the bottom food expenditure quintile.
Almost three-quarters of all loans are through formal institutions although this varies
to some extent across provinces. For example, half of loans held by households in
Quang Nam and Dak Lak are through the informal sector. Households in the poorest
wealth quintile are more likely to rely on informal sources, although 64 per cent of
loans taken out by the least wealthy are through the formal sector. On the basis of
these statistics it does not appear that access to formal financial institutions is
constrained for rural households in these provinces in Vietnam. Rather, they suggest
that co-funding of loans by formal and informal institutions may be a feature of
4
A description of all of the methods used in this paper is provided in the Appendix.
The survey was developed in collaboration between the Development Economics Research Group
(DERG), Department of Economics, University of Copenhagen and the Central Institute of Economic
Management (CIEM), the Institute for Labour Studies and Social Affairs (ILSSA) and the Institute of
Policy and Strategy for Agriculture and Rural Development (IPSARD), Hanoi, Vietnam.
6
All data expressed in VND are deflated to be made comparable across regions and time with the Red
River Delta in 2006 representing the base year and region. Details of the deflators used are provided in
the Appendix.
5
3
Vietnamese rural credit markets.7 These statistics are further validated by the results
for poor compared with non-poor households. Also of note is the fact that only 8 per
cent of households in 2006 and 6 per cent of households in 2008 reported that they
received less than they had applied for suggesting that credit is not rationed.
Table 1: Access to Credit 2006
Total
Total
% hhs amount amount
with borrowed owed as
loans
as %
% of
income income
Province
Ha Tay
Lao Cai
Phu Tho
Lai Chau
Dien Bien
Nghe An
Quang Nam
Khanh Hoa
Dak Lak
Dak Nong
Lam Dong
Long An
Proportion Formal by Purpose
Proportion Informal by Purpose
Total
Agricult.
Inv.
Cons.
Total
Agricult.
Inv.
Cons.
44.49
85.18
71.62
58.94
77.20
64.69
60.60
58.46
76.56
75.49
91.30
77.56
75.98
80.56
59.46
32.23
42.69
55.16
45.91
50.01
103.90
82.99
69.92
172.35
40.49
68.47
38.35
27.37
37.49
31.45
29.05
36.90
40.83
46.91
31.41
50.04
84.01
80.91
83.73
89.16
91.14
81.40
47.78
58.03
50.04
71.74
61.41
77.60
38.73
63.59
52.29
63.41
71.83
41.25
43.55
59.37
71.97
80.75
83.11
71.01
20.17
17.37
16.71
24.09
6.94
19.53
16.76
2.79
5.17
4.81
11.26
8.91
9.38
2.28
7.87
4.53
9.87
13.96
21.12
12.39
9.73
6.60
3.38
3.70
15.99
19.09
16.27
10.84
8.86
18.59
52.21
41.97
49.96
28.26
38.59
22.40
18.25
50.82
30.94
43.57
8.96
22.18
29.75
30.30
59.76
66.78
53.56
71.61
38.93
23.30
15.49
0.00
8.91
23.42
23.80
0.00
15.24
12.04
8.83
13.54
16.99
23.25
38.82
31.29
73.19
32.99
35.48
39.72
16.33
16.71
22.23
11.34
Poorest
2nd poorest
Middle
2nd richest
Richest
74.64
65.64
70.49
56.62
62.68
104.17
78.47
65.49
71.03
68.42
42.86
45.36
37.85
40.57
28.29
64.19
74.48
76.43
80.73
72.15
74.53
66.30
50.97
46.77
40.36
9.38
13.56
14.79
16.27
21.41
6.60
6.67
12.58
9.60
9.33
35.81
25.52
23.57
19.27
27.85
54.85
37.19
44.87
35.12
30.73
13.20
19.35
18.90
13.94
31.04
28.10
35.31
23.47
28.46
13.09
Poor1
Not Poor
75.70
63.37
97.91
71.00
63.21
31.60
63.37
75.92
63.60
53.06
9.45
16.34
11.68
9.04
36.63
24.08
49.46
40.00
13.32
21.60
33.48
20.82
Total
66.10
76.93
38.54
72.79
55.44
14.79
9.63
27.21
43.09
18.89
Note: Data are taken from the VARHS 2006 and are based on the five most important loans obtained in
the previous five years (2002 to time of survey in 2006). Only households that are also included in the
2008 data are used for comparability with later analysis. Weights are applied in the computation of
these statistics and so they are representative of rural households within the provinces surveyed.
1
Refers to households in the bottom food expenditure quintile.
24.97
Wealth Quintile
In terms of the purpose of the loans obtained we consider three different categories,
loans used for investment in agriculture, loans used for investment in land and other
assets, and loans used for consumption purposes. Investment loans are productive
forms of credit which in the future, if invested wisely, should enable the household to
improve their welfare (through higher levels of income, productivity, etc.).
Consumption loans, on the other hand, are non-productive but may be an important
instrument for smoothing consumption in the face of an exogenous shock. As
discussed in Section 1, evidence from the literature suggests that facilitating
7
Barnebeck Anderson and Malchow-Moller (2006) analyse the strategic interaction between formal
and informal lenders in undeveloped credit markets and show that under certain circumstances loans
will be co-funded. Our descriptive statistics appear to support this finding.
4
investment loans can lead to long run growth while flexibility in the availability of
consumption loans can have negative effects on development (Hung, 2005).
Unsurprisingly, we find that a greater proportion of informal credit is used for
consumption purposes. This may be due to the fact that access to credit through
formal financial institutions may require households to have a clear plan as to what
they will use the money for. Productive investments that have the potential to yield a
return are justifiable in this setting given that the return will cover the interest
payments. In contrast, credit for consumption may serve to worsen the financial
situation of households, particularly if interest payments are high. Informal loans for
consumption purposes are more prevalent among poor households. Furthermore, poor
households are more likely to use formal loans for agricultural purposes than for other
investment opportunities.
Given the importance of the VBSP and VBARD to rural credit markets, in Table 2 we
present similar descriptive statistics for loans from these sources disaggregated by
poor and non-poor households. VBARD loans account for 46 per cent of total formal
credit accessed by households in our survey while VBSP loans account for 35 per
cent. This highlights the importance of these two sources of formal credit for rural
households in Vietnam. In general we find that VBSP loans are used more for
agricultural investment and consumption purposes than loans from the VBARD. Of
particular note is the fact that poor households rely on the VBSP to a much greater
extent than non-poor households (55 per cent compared with 29 per cent,
respectively). It is also of interest to note that VBSP loans to poor households are
more likely to be for agriculture or consumption compared with VBSP loans to nonpoor household. Overall, it appears from these statistics that poor households rely on
the VBSP for access to formal credit, particularly for agricultural investments and
consumption loans.8 The determinants of access to credit and the effectiveness of
credit are explored further in the empirical analysis presented in the Section 3.
Particular focus is placed on the role of credit in enhancing the economic situation of
the poor.
Table 2: Access to Credit 2006 (VBSP vs. VBARD)
%
% VBSP
VBARD
in Total
in Total
Formal
Formal
Proportion VBSP by Purpose
Proportion VBARD by Purpose
54.5
28.70
27.54
51.84
Agricult.
72.47
66.81
Inv.
8.49
13.17
Cons.
11.94
9.03
Other.
7.10
10.98
Agricult.
57.15
57.35
Inv.
16.26
19.16
Cons.
8.08
9.30
Other.
18.52
14.18
Total
35.03
Note: As for Table 1.
45.82
68.94
11.41
10.13
9.51
57.32
18.70
9.11
14.87
Poor1
Not Poor
3. Empirical Analysis
3.1 Access to Credit
8
The extent to which this is demand driven or is associated with the specific targeted policies of the
VBSP cannot be deciphered from this analysis. Further investigation into the supply side of credit
markets will be required in order to determine the extent to which poor households have limited access
to formal credit other than the VBSP.
5
In order to construct a profile of the households that borrow we perform a number of
different regression analyses. We estimate a probit model of the determinants of the
probability of a household accessing credit and disaggregate this to consider the
determinants of access to credit from formal and informal sources separately. This
requires the construction a bivariate probit model that estimates the determinants of
both outcomes simultaneously. Estimating these models simultaneously allows the
factors that determine formal and informal credit to be jointly determined controlling
for correlations between the unobserved components of each individual probit
equation.9 We also explore access to formal credit further by analyzing the
determinants of access to VBSP loans and VBARD loans separately, also within a
bivariate probit framework.
The explanatory variables considered in each model are described in the Appendix.
The results of the probit models are presented in Table 3. The dependent variable in
the probit model presented in column (1) is a dummy indicator for whether the
household had a loan (from either formal or informal sources) in any of the 5 years
prior to 2006. Income is not an important factor in determining whether a household
had loans however wealth does appear to be an important factor. This may be due to
the fact that household wealth can be used as collateral and so wealthier households
are more likely to have access to credit. Income or ability to repay, on the other hand,
does not appear to be an important factor. This is further evidenced by the fact that
poor households, in the bottom food expenditure quintile, are more likely to have
loans. There is some evidence that households where the head of household has a
higher level of education are associated with a higher probability of having loans,
however, this effect disappears for the highest education levels. This may be due to
the fact that households with higher levels of education potentially have greater access
to information regarding financial institutions and may be more likely to understand
loan application procedures. This possibility will be explored further once the results
are disaggregated by the source of credit. Households with older household heads are
less likely to have loans, consistent with the lifecycle model of consumption and
saving. The greater the proportion of active household members who work, the less
likely the household is to have loans. We also find that households with savings are
less likely to have loans. Combined, these results suggest that some households
choose to either diversify their income (through more active household participants
earning an income) or save, rather than relying on credit. The greater the land area
owned by the household the more likely they are to have loans suggesting that having
collateral may be an important determinant of access. We find that households that
suffered an idiosyncratic shock are more likely to have loans suggesting that credit
might be an important coping mechanism in the face of idiosyncratic adverse income
shocks.
In columns (2) and (3) we disaggregate loans by source (formal and informal). This
model is estimated within a bivariate probit framework to control for correlations in
the unobserved components of each individual model. The first important difference
between households accessing formal credit and informal credit is that poorer
households are more likely to have informal credit. While education does not appear
to affect the probability of having informal loans it has a positive and significant
effect on the probability of having formal loans, although not at the highest education
9
Details of the methodological approach are provided in the Appendix.
6
levels. This is consistent with our suggestion that more educated households may have
more information regarding formal credit institutions and may be better able to
understand the application process. The total land area owned has a positive and
significant effect on access to formal credit, which is consistent with our suggestion
that land an important source of collateral for accessing formal credit. Barslund and
Tarp (2003) find a similar result in their analysis of a sample of 932 households from
rural Vietnam in 2002. They highlight a statistically significant difference in total land
holdings among households that were approved for formal loans compared with those
that were rejected. On the basis of our results, the importance of land holdings for
access to credit, in particular, formal credit appears to still hold. Households suffering
idiosyncratic shocks are more likely to have both forms of credit although the
magnitude of the effect is larger for informal loans. We also find that households
suffering income shocks due to natural disasters are more likely to have loans from
formal sources.
Table 3: Access to Credit
Bivariate Probit
Bivariate Probit
Probit
Credit
Formal Credit Informal Credit
VBSP Loans
VBARD Loans
(1)
(2)
(3)
(4)
(5)
-0.071 (0.388)
-0.372 (0.486) -1.074** (0.450) -1.014** (0.398)
0.722* (0.412)
Constant
0.0005 (0.001)
-0.002 (0.002)
0.001 (0.001)
-0.0003 (0.001) 0.0001 (0.001)
Income
0.023 (0.102)
0.254** (0.110) 0.260** (0.110) -0.302*** (0.110)
0.247** (0.112)
Poor
0.077 (0.126)
-0.015 (0.136)
0.133 (0.137)
-0.110 (0.135)
-0.037 (0.135)
Wealth Quint 2
0.112 (0.126)
0.055 (0.136)
0.079 (0.141)
0.081 (0.130)
0.112 (0.137)
Wealth Quint 3
0.079 (0.137)
-0.286* (0.156)
0.159 (0.159)
-0.076 (0.141)
-0.078 (0.146)
Wealth Quint 4
-0.018 (0.144)
-0.061 (0.158)
0.029 (0.178)
0.016 (0.147)
-0.033 (0.153)
Wealth Quint 5
0.363***
(0.111)
-0.167
(0.122)
0.112
(0.128)
0.242**
(0.116)
0.196* (0.118)
Education 2
0.224* (0.128) 0.356*** (0.115)
0.323*** (0.119) 0.453*** (0.111) -0.047 (0.122)
Education 3
0.256* (0.148)
-0.219 (0.168)
0.273 (0.176)
0.236 (0.152)
0.130 (0.155)
Education 4
0.245 (0.281)
0.109 (0.326)
-0.506 (0.482)
0.122 (0.288)
0.134 (0.287)
Education 5
-0.003
(0.003)
-0.009**
(0.003)
-0.003(0.004)
0.005*
(0.003)
-0.007**
(0.003)
Age
0.059**
(0.024)
-0.038
(0.027)
0.021
(0.027)
0.042*
(0.025)
0.019 (0.026)
HHsize
-0.319 (0.277)
-0.736** (0.299) -0.630** (0.278) -0.0003 (0.327) -0.324 (0.321)
HH_work_prop
-0.277 (0.188)
-0.350** (0.176) -0.350** (0.177) -0.358* (0.210) -0.320 (0.250)
Formal Saving
-0.018 (0.136)
-0.253 (0.159)
-0.007 (0.170)
0.078 (0.135)
-0.102 (0.141)
Informal Saving
-0.198** (0.093) -0.153* (0.088) -0.233** (0.100) -0.223* (0.105) -0.075 (0.092)
Home Saving
0.002 (0.001)
0.0005 (0.001)
0.006* (0.003) 0.009*** (0.003) -0.003 (0.003)
Total Area Owned
0.177** (0.089)
0.119 (0.097)
0.216** (0.101)
0.018 (0.092)
0.110 (0.095)
Shock: Natural
0.370 (0.401)
0.132 (0.377)
...
...
-0.068 (0.472)
Shock: Economic
Shock: Idiosyncratic 0.471*** (0.111) 0.224** (0.099) 0.545*** (0.103) 0.306*** (0.112) 0.133 (0.098)
Yes
Yes
Yes
Yes
Yes
Province Dummies
-1,350.95
-1,223.66
-668.17
Log Likelihood
1,233
1,233
1,233
N
Standard errors are given in parenthesis, *** denotes significance at the 1 per cent level, ** denotes
significance at the 5 per cent level, * denotes significance at the 10 per cent level.
Economic shocks are excluded from the bivariate probit model of VBSP vs. VBARD loans as no
households with VBSP loans suffered an economic shock causing perfect multicollinearity in the
model.
In columns (4) and (5) we focus on the factors related to households’ decisions to
access credit via the VBSP and the VBARD. The dependent variable in the model
presented in column (4) takes a value of one if the household has a loan specifically
with the VBSP and zero otherwise. Similarly, the dependent variable in the model
7
presented in column (5) takes a value of one if the household has a loan specifically
with the VBARD and zero otherwise. The determinants of loans from the VBSP are
similar to those for informal credit (column (3)). As expected, poor households are
more likely to have loans with the VBSP and are less likely to have loans with the
VBARD. The expected effect of age and households size on access to formal credit is
evident for the VBARD loans but not for loans from VBSP. Education is an important
factor for accessing VBARD loans but less so for loans from the VBSP. We also find
that households suffering adverse income shocks rely greatly on loans from the VBSP
but not at all from the VBARD. Overall these results are consistent with what we
might expect given that the VBSP offers structured lending at low interest rates for
those in need. In contrast, the VBARD operates on a commercial basis with the
expected access constraints for poor, less educated households evident.
Overall, these results suggest that while access to credit in rural Vietnam is very high,
the sources of credit vary considerably across different household groups. If the
effectiveness of obtaining credit on welfare outcomes varies by source of loan
(formal/informal, VBSP/VBARD) then this may have important consequences for
policies aimed at reducing poverty. This is particularly the case since informal credit
is more associated with poorer and less educated households, while formal credit is
more associated with educated and wealthier households. Furthermore, the only
source of formal credit accessible by the poor is through the VBSP.
3.2 Use of Loans
To further consider the profile of households accessing credit (in 2006) we analyze
the factors determining the use of the loans accessed by households. Since households
may hold many loans for different purposes we construct a variable which measures
the share of total loans held by the household by purpose. We consider four separate
budget shares, the share used for agricultural investment, the share used for
investment in assets and land, the share used for consumption and an ‘other’ category
that encompasses all other types of uses for loans. Using the share of total loans by
purpose rather than the value of loans by purpose allows us to reveal trade-offs
between the different purposes for which credit is used. For example, are certain
households more likely to access credit for agricultural investments than
consumption? If all households in our sample accessed credit then these models could
be estimated using standard regression techniques. However, since not all households
borrow and those that do are a selected sample, the standard ordinary least squares
approach will yield biased and inconsistent estimates of the parameters. In other
words, using standard techniques, the factors that determine whether the household
has access to credit are not controlled for when considering the determinants of the
purpose of the loans. We can control for selection bias using a Heckman sample
selection model where in the first stage we model the probability of having a loan and
use the inverse mills ratio, constructed from the parameter estimates of this first stage
model, to correct for sample selection in the second stage. Details of this approach can
be found in the Appendix.
We run a Heckman sample selection model for the proportional amount of credit
attributable to each source that controls for the factors determining access to credit.
The dependent variables considered are: 1) the share of credit for agriculture
investment; 2) the share of credit used for investments in land and other assets; 3) the
share of credit used for consumption purposes and 4) an ‘other’ category which
8
includes non-farm activity, repayment of other loans and other purposes. The model is
estimated only for households with loans in 2006. The results are presented in Table
4.
Table 4: Heckman Sample Selection models of loans by purpose (proportion of credit by
purpose)
Agriculture
Land and Asset
Consumption
Other
Investment
Investment
-0.057 (0.143)
0.351*** (0.125)
0.033 (0.226)
0.709*** (0.233)
Constant
0.0003 (0.0003)
-0.0003 (0.0003)
0.001* (0.0005)
-0.001 (0.001)
Income
-0.024 (0.034)
0.046 (0.030)
0.084 (0.055)
-0.100* (0.057)
Poor
-0.011 (0.036)
0.022 (0.032)
-0.019 (0.059)
-0.002 (0.061)
Wealth Quint 2
0.002 (0.036)
0.012 (0.032)
0.096 (0.060)
-0.121** (0.062)
Wealth Quint 3
0.014
(0.041)
0.045
(0.036)
0.057 (0.066)
-0.131* (0.068)
Wealth Quint 4
0.069 (0.044)
0.001 (0.038)
0.076 (0.070)
-0.159** (0.072)
Wealth Quint 5
0.013 (0.034)
-0.007 (0.030)
0.066 (0.055)
-0.080 (0.057)
Education 2
0.011 (0.039)
-0.009 (0.034)
0.160** (0.063)
-0.165** (0.065)
Education 3
0.090* (0.045)
-0.018 (0.040)
0.060 (0.073)
-0.113 (0.075)
Education 4
-0.008
(0.089)
0.056
(0.078)
0.163 (0.140)
-0.157 (0.145)
Education 5
-0.0005 (0.001)
0.002*** (0.001)
-0.002 (0.002)
0.0003 (0.002)
Age
0.0004 (0.007)
-0.019*** (0.006)
0.0003 (0.012)
0.018 (0.012)
HHsize
0.122 (0.096)
-0.166** (0.084)
-0.320** (0.154)
0.370** (0.160)
HH_work_prop
-0.0003 (0.0003)
-0.0005 (0.003)
0.001 (0.001)
0.0003 (0.0006)
Total Area Owned
-0.032
(0.024)
-0.032
(0.021)
-0.015
(0.034)
0.084**
(0.035)
Shock: Natural
-0.074
(0.104)
-0.058
(0.090)
0.066
(0.156)
0.082 (0.162)
Shock: Economic
0.021 (0.041)
0.144*** (0.036)
0.023 (0.066)
-0.191*** (0.068)
Shock: Idiosyncratic
Yes
Yes
Yes
Yes
Province Dummies
Note: Standard errors are given in parenthesis, *** denotes significance at the 1 per cent level, **
denotes significance at the 5 per cent level, * denotes significance at the 10 per cent level. Results for
the selection equation are not presented but are available on request. The total number of observations
is 1,233. The number of censored observations is 914.
Income is found to have a significant positive effect on the share of credit used for
‘other’ purposes (such as business expansion, for example) suggesting that these
activities are more associated with higher income households. We also find that
households in the upper wealth quintiles hold a smaller proportion of loans for the
purpose of investing in agriculture.10 The higher the education level of the head of
household the lower the share of credit used for agricultural investments. In contrast,
there is some evidence to suggest that higher education levels are associated with
holding a greater share of credit for ‘other’ activities. Older households are more
likely to access credit for consumption purposes. This may suggest that the elderly are
more at risk of poverty given that they are more likely to rely on credit for
consumption. Larger households and households with a greater proportion of active
household members in employment are more likely to hold a greater share of credit
for agricultural investments. They also hold a smaller share of credit for consumption
purposes. This suggests that larger, more diversified, households are less exposed to
the risk of relying on credit for consumption purposes. Households that have suffered
an income shock due to a natural disaster hold a greater share of credit for agricultural
investments. This is not surprising given that in the wake of a natural disaster farmers
10
The results for households in the lower food expenditure quintiles are in slight contrast to this
suggesting that poorer households in terms of per capita food expenditure hold fewer loans for
agricultural purposes, although the result is only weakly significant.
9
are more likely to need to invest in rebuilding their farming activities. Households that
suffer an idiosyncratic shock hold a greater proportion of credit for consumption
purposes suggesting that credit is an important coping mechanism in the face of
income shocks.
The analysis presented in this section highlights the very different roles that credit
plays in the lives of rural Vietnamese households. The use of the loans obtained varies
significantly by household characteristics. High income households and more
educated households are more likely to use credit to invest in land or assets or other
non-farm activities such as enterprise development. The non-productive use of credit,
i.e. for consumption purposes, is associated with older households and those who have
faced an idiosyncratic adverse income shock. These observations suggest that
understanding the effectiveness of credit in improving outcomes will be important in
the design of policies aimed at improving rural credit markets. In particular, it is
important to understand the extent to which the source of credit obtained, and how
those funds are used, impacts on outcomes for different household groups.
3.3 Effectiveness of Credit
Having established the profile of the households that borrow and the reasons why they
borrow, we proceed to the third stage of the analysis. Here we attempt to establish the
extent to which access to credit improves outcomes. In particular, we focus on poverty
reducing determinants such as income, specialization, investment and productivity.
These models, however, suffer from a potential endogeneity problem due to the fact
that the unobserved factors that affect each dependent variable may be correlated with
the unobserved factors that determine whether a household has access to credit
leading to biased parameter estimates unless the unobserved factors are controlled for
in the model. We address this issue in two ways. First, we use the selected sample of
households who have access to credit in analyzing the impact of the amount of credit
on outcomes. We control for access to credit using a Heckman sample selection
model where the determinants of access to credit are estimated in the first stage (as in
Table 3) and use the inverse mills ratio, constructed from the parameter estimates of
this model, to correct for sample selection in the second stage. Where we consider the
different effects of formal compared with informal credit we restrict our sample to
households who have access to credit and control for sample selection associated with
access to formal financial institutions. Similarly, where we disaggregate formal credit
into credit accessed through the VBSP, the VBARD and ‘other’ financial institutions
(including other state-owned commercial banks, local authorities, private banks,
people’s credit funds and local authorities) we restrict our sample to households who
have access to formal financial institutions and control for sample selection associated
with households who access commercial financial institutions.
Second, we estimate the second stage model using first differences thus controlling
for any unobserved time invariant heterogeneity that is specific to the household. This
may include, for example, the household’s level of risk aversion which may affect
both changes in outcomes and access to credit.11 As a further control for differences
in the level of financial prudence across households (which may affect both outcomes
and willingness to borrow) we draw on the extensive information included in the data
on shocks and risk-coping. We include an indicator variable for whether the
11
In most inter-temporal utility maximising models, risk aversion is assumed to be constant across time
thus validating the use of this assumption here.
10
household experienced an income shock between 2006 and 2008 to control for
exogenous factors that may require households to borrow more (or exert more
financial prudence). We also include an indicator for whether the household managed
to fully recover from prior income shocks to capture the household’s financial
management capabilities. We also include controls for changes in observable factors
such as wealth, the number of working household members, etc. The full list of
variables considered is provided in the Appendix along with a more detailed
exposition of the methodological approach. As in the first stage of our analysis we are
particularly interested in the differential effects for poor vs. non-poor households and
so, where possible, disaggregate the results for these groups.
Income
We first consider the impact of having credit in 2006 on the change in annual
household income between 2006 and 2008. We include controls for changes in
wealth, household size, the proportion of active household members who work,
whether the household experienced an adverse income shock between these years and
whether the household has recovered from a prior income shock. The results for total
household income are presented in Table 5.1a. After controlling for sample selection,
we find that the amount of credit held in 2006 has a positive and significant effect on
the change in annual income between 2006 and 2008. As discussed above, in order to
consider the different effects that formal and informal credit may have on income, we
must control for the fact that the unobserved factors that influence whether a
household has access to formal credit may also influence household income changes.
For example, it may be the case that households accessing formal credit have greater
income earning ability than those that rely more on informal credit potentially biasing
our results. Restricting our sample to households who access credit and controlling for
access to formal credit using the Heckman selection approach will correct for these
potential biases. In column (2) we find that the positive effect of credit on income
levels is driven by credit obtained from formal sources. In column (3) we find no
significant difference in the source of the formal credit obtained.12 The purpose of the
loan obtained also matters. The amount of credit used for investment in land and other
assets has a negative effect on income while loans obtained for ‘other’ purposes have
a positive and significant effect. The former may be explained by the fact that returns
to capital investments may take longer to realize and so may not be reflected in
income changes for some time. Column (5) reveals that these effects only hold for
formal credit.
The results for the control variables are also of interest. We find a positive and
significant relationship between household wealth and income changes and the
proportion of working household members and income changes. Both are in line with
expectations. We do not find any other control variables of statistical significance.
12
In this model we restrict our sample to households who access formal credit and control for the fact
that the unobserved factors that determine access to commercial formal credit as compared with VBSP
credit may be also correlated with income changes.
11
Table 5.1a: Effect of credit on total income (all households)
(1)
(2)
(3)
(4)
(5)
-9.381 (15.322) 15.857** (7.480) 13.518 (11.141) -10.213 (15.335) -12.743 (15.590)
Constant
0.065* (0.038)
Total credit obtained
0.089*** (0.032)
Formal credit
-0.181 (0.176)
Informal credit
Formal (VBSP)
-0.999 (1.246)
Formal (VBARD)
0.066 (0.063)
Formal (Other)
0.128 (0.087)
-0.002 (0.096)
Credit (Agriculture)
-0.266*** (0.082)
Credit (Land/Asset)
-0.134 (0.320)
Credit (Cons.)
0.134*** (0.042)
Credit (Other)
0.096 (0.136)
Formal (Agriculture)
-0.310*** (0.104)
Formal (Land/Asset)
-0.085 (0.613)
Formal (Cons.)
0.149***
(0.044)
Formal (Other)
-0.070 (0.247)
Informal (Agriculture)
-0.204 (0.225)
Informal (Land/Asset)
-0.160 (1.192)
Informal (Cons.)
-0.094 (0.353)
Informal (Other)
-1.358
(5.597)
-0.471
(5.075)
-2.639
(5.605)
-1.675 (5.666)
Poor (2006)
-1.526 (8.756)
Wealth quintile (change) 5.750*** (2.028) 6.229*** (1.767) 5.891*** (2.173) 6.257*** (2.012) 6.301*** (2.029)
HH_work_prop (change) 36.99*** (12.599) 32.14*** (11.107) 40.932 (14.819) 37.297*** (12.50) 37.978*** (12.58)
-1.073 (2.158) -0.936 (1.860) -1.942 (2.695) -1.222 (2.137) -1.203 (2.138)
HHsize (change)
-0.727 (4.701) 1.812 (4.221) 5.462 (5.926) -1.199 (4.675) -1.663 (4.691)
Shock: Natural
6.696 (7.233) 6.761 (6.356) 9.381 (8.344) 6.661 (7.172) 6.193 (7.179)
Shock: Economic
1.221 (6.828) 1.160 (6.179) 4.714 (8.479) 1.805 (6.759) 1.699 (6.770)
Shock: Idiosyncratic
3.289 (5.281) 0.381 (4.657) 0.229 (6.410) 3.151 (5.252) 3.489 (5.292)
Recovered from shock
Yes
Yes
Yes
Yes
Province Dummies
Yes
1,220
859
1,220
1,220
n
714
Note: Standard errors are given in parenthesis, *** denotes significance at the 1 per cent level, **
denotes significance at the 5 per cent level, * denotes significance at the 10 per cent level. Heckman
Sample Selection Model. The results for the selection equation for each model are available on request.
Given the focus of this paper we also consider the impact of credit on poor households
in isolation.13 The results are presented in Table 5.1b. We find a very different
relationship between loans and changes in income for poor households. We find a
larger positive and significant effect of credit on income changes. Once we
disaggregate credit into formal and informal credit, and include the relevant controls
for selection bias, we find that only informal credit has a significant effect on income
changes. We cannot distinguish between the effectiveness of different sources of
credit which is not surprising given that formal credit does not significantly affect
income changes for poor households. The most important purpose for which credit is
accessed is investment in agriculture, through both formal and informal sources.
There is also some evidence to suggest that credit accessed for consumption purposes,
through informal sources has a positive effect on household income. In fact, the
inclusion of this disaggregation renders the coefficient on idiosyncratic shocks
positive and significant suggesting that informal credit may be an important
mechanism for smoothing income and possibly consumption in the face of such
income shocks. The control variables are also different for poor households. Changes
13
We define poor households as those in the bottom food expenditure quintile.
12
in wealth and the proportion of adults working do not appear to impact on income
changes while changes in household size have a positive and significant effect. We
also find that poor households are more likely to suffer significant income losses due
to economic shocks.
Table 5.1b: Effect of credit on total income (poor households)
Constant
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Wealth quintile (change)
HH_work_prop (change)
HHsize (change)
Shock: Natural
Shock: Economic
Shock: Idiosyncratic
Recovered from shock
Province Dummies
n
Note: As for Table 5.1a.
(1)
(2)
(3)
(4)
(5)
2.944 (6.279) 8.570 (6.941) 10.194 (12.666) 0.245 (6.133) 3.044 (6.137)
0.496*** (0.114)
0.127 (0.178)
0.802*** (0.197)
-0.897 (1.069)
0.218 (0.270)
0.222 (0.782)
0.644*** (0.111)
0.108 (0.281)
0.523** (0.228)
-0.168 (0.182)
0.617*** (0.171)
0.259 (0.408)
0.227 (0.441)
-0.090 (0.198)
0.768*** (0.161)
1.240 (1.052)
1.295* (0.732)
-1.533 (3.838)
-1.979 (2.018) -2.080 (2.342) -4.086 (3.940) -2.757 (1.975) -2.780 (2.006)
13.677 (11.611) 17.185 (17.251) 51.655* (27.259) 8.806 (11.252) 9.031 (11.355)
5.459*** (1.252) 5.829*** (1.441) 7.981** (3.537) 6.163*** (1.217) 6.119*** (1.217)
0.584 (2.323) 1.030 (2.845) 5.335 (5.866) 0.850 (2.243) 0.540 (2.248)
-7.178** (3.342) -9.255** (4.181) -15.394** (7.502) -6.523** (3.224) -7.570** (3.311)
4.289 (3.679) 5.721 (4.628) 5.817 (8.537) 6.147* (3.561) 5.415 (3.598)
2.103 (2.680) 2.573 (3.225) 6.541 (6.481) 1.978 (2.576) 2.189 (2.632)
Yes
Yes
Yes
Yes
Yes
297
231
297
297
177
To further explore the effects of access to credit we disaggregate income changes into
three different components: agricultural income, rental income and non-farm
income.14 The results for agricultural income are presented in Tables 5.2a and 5.2b.
The overall amount of credit held in 2006 does not have a statistically significant
effect on the change in agricultural income between 2006 and 2008. We do find,
however, that formal credit accessed through other sources (i.e. from formal sources
other than the VBSP and the VBARD) has a positive and significant effect. Once the
purpose of the loans is taken into account we find that loans used for investment in
agriculture from formal sources have a positive and significant effect on the change in
agricultural income. In contrast, loans obtained for investment in land and assets
(from formal sources) have a negative effect on agricultural income. This suggests
that households using loans for investment in land and assets are possibly moving
14
We also considered the impact of credit on wage income but no statistically significant relationship
was found.
13
away from agriculture as their source of income, choosing to invest in alternative
activities.
Table 5.2a: Effect of credit on agricultural income (all households)
(1)
(2)
9.241* (4.994) 5.489* (3.110)
Constant
0.013 (0.013)
Total credit obtained
0.010 (0.013)
Formal credit
0.007 (0.072)
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
-3.034* (1.819) -0.396 (2.109)
Poor (2006)
Wealth quintile (change) 0.845 (0.666) 0.457 (0.727)
HH_work_prop (change) -2.385 (4.125) -5.928 (4.588)
0.282 (0.709) 0.202 (0.768)
HHsize (change)
1.734 (1.544) 0.918 (1.737)
Shock: Natural
3.414 (2.373) 1.985 (2.619)
Shock: Economic
0.506 (2.244) 0.397 (2.541)
Shock: Idiosyncratic
1.741 (1.728) 0.264 (1.920)
Recovered from shock
Yes
Yes
Province Dummies
1,220
859
N
Note: As for Table 5.1a.
(3)
(4)
-1.101 (4.650) 9.108* (4.932)
(5)
6.586 (4.920)
0.170 (0.494)
-0.019 (0.026)
0.071** (0.037)
0.123*** (0.031)
-0.107*** (0.027)
-0.135 (0.104)
0.013 (0.014)
-6.176* (3.638)
0.664 (0.883)
-7.556 (6.098)
0.837 (1.080)
1.273 (2.375)
2.020 (3.345)
2.262(3.417)
-0.684 (2.608)
Yes
714
-2.940* (1.793)
0.835 (0.659)
-2.277 (4.059)
0.206 (0.699)
1.165 (1.528)
3.249 (2.340)
0.652 (2.212)
1.321 (1.704)
Yes
1,220
0.249*** (0.044)
-0.097*** (0.034)
-0.029 (0.199)
0.013 (0.014)
0.013 (0.079)
-0.025 (0.074)
-0.148 (0.384)
0.065 (0.117)
-1.994 (1.771)
0.677 (0.658)
-2.430 (4.031)
0.216 (0.692)
0.766 (1.519)
2.943 (2.318)
0.495 (2.197)
1.982 (1.694)
Yes
1,220
We further explore the extent to which access to credit impacts on agricultural income
by considering poor households in isolation. The results are presented in Table 5.2b.
For poor households, only the level of formal credit has a positive and significant
effect on changes in income, however, we cannot determine which source of formal
credit is of most importance. Credit accessed for agricultural investment, through both
formal and informal sources, has a positive and significant effect on the change in
agricultural income.
14
Table 5.2b: Effect of credit on agricultural income (poor households)
(1)
-0.477 (3.869)
Constant
0.222*** (0.070)
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Wealth quintile (change) -0.247 (1.244)
HH_work_prop (change) 8.493 (7.154)
0.762 (0.771)
HHsize (change)
2.220 (1.430)
Shock: Natural
1.319 (2.059)
Shock: Economic
1.070 (2.266)
Shock: Idiosyncratic
0.482 (1.651)
Recovered from shock
Yes
Province Dummies
297
n
Note: As for Table 5.1a.
(2)
1.750 (4.229)
(3)
-0.060 (7.133)
(4)
-1.308 (3.865)
(5)
-2.109 (3.856)
0.222** (0.112)
0.155 (0.124)
0.223 (0.606)
0.234 (0.154)
-0.058 (0.444)
0.278*** (0.070)
0.022 (0.177)
0.151 (0.144)
-0.068 (0.115)
-0.917 (1.455)
16.329 (10.606)
1.071 (0.897)
2.524 (1.781)
0.558 (2.587)
0.444 (2.915)
0.242 (2.017)
Yes
231
-0.748 (2.226)
40.359*** (15.28)
0.763 (2.015)
7.755** (3.349)
1.365 (4.271)
1.415 (4.875)
2.691 (3.691)
Yes
177
-0.623 (1.245)
6.825 (7.089)
1.087 (0.769)
2.357* (1.417)
1.677 (2.036)
1.791 (2.250)
0.380 (1.626)
Yes
297
0.441*** (0.107)
0.141 (0.255)
0.446 (0.278)
-0.055 (0.124)
0.186* (0.102)
0.574 (0.655)
0.462 (0.458)
-0.106 (2.395)
-0.840 (1.260)
7.076 (7.137)
1.112 (0.760)
2.298* (1.404)
1.358 (2.071)
2.282 (2.461)
0.383 (1.649)
Yes
297
The effect of credit on rental income is explored in Tables 5.3a and 5.3b. Overall, the
amount of credit held in 2006 has a negative and significant effect on the change in
rental income between 2006 and 2008. However, as revealed in Column (2), this
negative effect is driven entirely by formal credit (accessed through sources other than
the VBSP and VBARD – see Column (3)). Once credit is disaggregated by purpose
of the loan we find that credit obtained for investment in land and assets has a positive
effect, as might be expected, while credit obtained for other purposes has a negative
effect. The former is driven entirely by credit accessed through informal sources while
the latter is driven by credit accessed through formal sources. Two possible
explanations for this finding are that: 1) returns to buying land take longer to realize
and so we may not see any effect (or even a negative effect) for some time after the
credit has been obtained and the land transaction has been made; and 2) the amount
required to purchase land are so high that formal financial institutions are less likely
to offer loans for this purpose and as a result households tend to rely on loans from the
informal sector. The negative effect of credit accessed for ‘other’ purposes on rental
income suggests that households accessing this type of credit are moving away from
the rental of land and other assets as a source of income. This is also evidenced in
Table 5.4a where we find that credit obtained for ‘other’ purposes has a positive effect
on non-farm, non-wage income. It is interesting to note that the positive effect on
rental income of credit obtained for the purpose of investing in land and assets is
15
driven by credit obtained in the informal sector (Table 5.3a, Column (5)) while the
positive effect of credit obtained for ‘other’ purposes on non-farm, non-wage income
is driven by credit obtained in the formal sector (Table 5.4a, Column (5)), and in
particular, sources other than the VBSP and VBARD (Table 5.4a, Column (3)).
Table 5.3a: Effect of credit on rental income (all households)
Constant
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Poor (2006)
Wealth quintile (change)
HH_work_prop (change)
HHsize (change)
Shock: Natural
Shock: Economic
Shock: Idiosyncratic
Recovered from shock
Province Dummies
n
Note: As for Table 5.1a.
(1)
(2)
(3)
(4)
(5)
1.338 (1.435) 2.457*** (0.875) 1.954 (1.386) 1.311 (1.427) 0.843 (1.408)
-0.013*** (0.004)
-0.016*** (0.004)
-0.002 (0.020)
0.027 (0.150)
0.005 (0.008)
-0.046*** (0.011)
0.003 (0.009)
0.013* (0.008)
0.016 (0.030)
-0.021*** (0.004)
0.021* (0.012)
-0.014 (0.010)
0.020 (0.057)
-0.020*** (0.004)
-0.025 (0.023)
0.134*** (0.021)
0.043 (0.110)
0.018 (0.033)
0.119 (0.522) -0.296 (0.594) -0.760 (1.086) 0.248 (0.518) 0.400 (0.507)
-0.482** (0.192) -0.347* (0.205) -0.482* (0.265) -0.534 (0.191) -0.561*** (0.188)
-0.327 (1.189) -0.956 (1.293) -2.013 (1.825) -0.341 (1.176) -0.204 (1.153)
0.035 (0.205) 0.074 (0.217) 0.142 (0.326) 0.041 (0.203) 0.078 (0.198)
-0.861* (0.446) -0.757 (0.490) -1.181* (0.717) -0.878** (0.443) -0.798* (0.434)
-1.760*** (0.685) -1.885** (0.739) -2.477** (1.010) -1.780*** (0.679) -1.739*** (0.663)
0.093 (0.648) 0.143 (0.717) 0.398 (1.029) 0.028 (0.642) -0.202 (0.628)
-0.057 (0.498) -0.042 (0.452) -0.120 (0.783) -0.063 (0.494) 0.078 (0.485)
Yes
Yes
Yes
Yes
Yes
1,220
859
1,220
1,220
714
In relation to the control variables, it is also clear from Table 5.3a that increases in
wealth are negatively related to changes in rental income but, as revealed in Table
5.4a, are positively related to changes in non-farm, non-wage income. This supports
our hypothesis that as households become wealthier they shift away from rental
income towards business activities. We also find that natural and economic shocks
have a significant negative effect on rental income. This may be due to the destruction
of land and property in the aftermath of a natural disaster to the extent that it can no
longer be rented or a general decline in economic conditions in the region shrinking
the market for rental assets. In contrast, we find that households that suffer an
economic shock experience a greater change in non-farm, non-wage income than
other households. Coupled with the negative effect on rental income, this may suggest
that households switch to other income earning activities (such as starting their own
business) in the event of unanticipated economics shocks that threaten their source of
income.
16
Table 5.4a: Effect of credit on non-farm, non-wage income (all households)
Constant
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Poor (2006)
Wealth quintile (change)
HH_work_prop (change)
HHsize (change)
Shock: Natural
Shock: Economic
Shock: Idiosyncratic
Recovered from shock
Province Dummies
n
Note: As for Table 5.1a.
(1)
(2)
(3)
(4)
(5)
-19.76** (9.500) -2.883 (6.132) -1.804 (9.192) -18.54** (9.424) -19.15** (9.512)
0.141*** (0.024)
0.149*** (0.027)
-0.039 (0.144)
-0.664 (1.020)
0.081 (0.052)
0.256*** (0.072)
-0.093 (0.059)
-0.049 (0.051)
-0.090 (0.197)
0.196*** (0.026)
-0.131 (0.083)
-0.084 (0.064)
-0.130 (0.375)
0.220*** (0.027)
-0.060 (0.151)
0.073 (0.138)
0.022 (0.729)
-0.188 (0.216)
1.840 (3.473) 0.452 (4.160) 1.957 (7.219) 0.271 (3.443) 0.075 (3.455)
3.631*** (1.254) 4.438*** (1.449) 4.028** (1.785) 4.087*** (1.238) 4.378*** (1.241)
18.084** (7.801) 18,822** (9.107) 26.168** (12.199) 17.964** (7.687) 19.367** (7.684)
-0.800 (1.335) -0.712 (1.525) -1.060 (2.209) -0.814 (1.315) -0.777 (1.307)
2.704 (2.908) 3.150 (3.461) 7.526 (4.858) 3.097 (2.876) 3.114 (2.869)
11.617*** (4.475) 12.130** (5.212) 17.259** (6.841) 11.984*** (4.412) 11.910*** (4.389)
-3.722 (4.223) -3.418 (5.067) -4.832 (6.958) -3.261 (4.159) -3.730 (4.140)
0.232 (3.270) -1.164 (3.819) -1.324(5.267) 0.501 (3.229) 0.047 (3.233)
Yes
Yes
Yes
Yes
Yes
1,220
859
1,220
1,220
714
Considering the impact of credit on rental incomes and non-farm, non-wage incomes
of poor households in isolation also reveals some interesting insights. We find that,
for the most part, credit has no impact on either the rental income or non-farm, nonwage income of the poor (Tables 5.3b and 5.4b). However, once we disaggregate by
source of formal credit (Table 5.3b, column (3)) we find that credit accessed by poor
households from commercial financial institutions (other than VBSP and VBARD)
has a positive and significant effect. The only other factor of significance for changes
in rental income of the poor is whether they suffered from an idiosyncratic shock. We
find that poor households that suffer from such a shock experience an increase in their
rental income. This may be due to the fact that they switch to renting property or
assets as an income earning activity when faced with difficult economic times.
17
Table 5.3b: Effect of credit on rental income (poor households)
(1)
0.438** (0.192)
Constant
-0.002 (0.003)
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Wealth quintile (change) -0.020 (0.062)
HH_work_prop (change) -0.101 (0.354)
-0.049 (0.039)
HHsize (change)
-0.008 (0.072)
Shock: Natural
-0.085 (0.103)
Shock: Economic
0.121 (0.114)
Shock: Idiosyncratic
0.149*
(0.083)
Recovered from shock
Yes
Province Dummies
297
n
Note: As for Table 5.1a.
(2)
(3)
(4)
(5)
0.429** (0.220) -0.097 (0.414) 0.422** (0.194) 0.423** (0.195)
0.001 (0.006)
-0.002 (0.006)
0.025 (0.035)
0.005 (0.009)
0.044* (0.026)
-0.002 (0.003)
0.003 (0.009)
0.007 (0.007)
-0.002 (0.006)
-0.016 (0.076)
-0.216 (0.551)
-0.051 (0.047)
-0.021 (0.093)
-0.082 (0.135)
0.185 (0.152)
0.189* (0.105)
Yes
231
-0.109 (0.129)
-0.093 (0.889)
-0.154 (0.116)
0.010 (0.193)
-0.343 (0.247)
0.198 (0.281)
0.460** (0.213)
Yes
177
-0.002 (0.005)
0.004 (0.013)
0.001 (0.014)
-0.001 (0.006)
-0.003 (0.005)
-0.002 (0.034)
0.015 (0.023)
-0.040 (0.124)
-0.018 (0.063) -0.019 (0.064)
-0.128 (0.356) -0.132 (0.361)
-0.053 (0.039) 0.051 (0.039)
-0.009 (0.072) -0.011 (0.072)
-0.092 (0.104) -0.097 (0.106)
0.126 (0.115) 0.117 (0.116)
0.153* (0.082) 0.163 (0.084)
Yes
Yes
297
297
For non-farm, non-wage income we find some evidence to suggest that formal credit
accessed through the VBSP by poor households has a negative effect on non-farm,
non-wage income. This may be due to the fact that these funds are used for
specialization in other activities such as agriculture. However, the fact that we do not
observe a corresponding positive effect of VBSP lending on agricultural income
questions the effectiveness of this lending. This will be explored further in later
sections. In contrast to the aggregate results, wealth and the proportion of working
adults in the household are negatively associated with changes in non-farm, non-wage
income for poor households. This may suggest that the types of income earning
activities in this category may be different for poor households compared with nonpoor households where these variables have a positive effect. For example, for poor
households, non-farm, non-wage income could include income from the use of
common property resources which may be more associated with less wealthy
households with more inactive household members.
18
Table 5.4b: Effect of credit on non-farm, non-wage income (poor households)
(1)
(2)
(3)
(4)
(5)
0.793 (1.701) 1.603 (1.503) 5.209** (2.576) 0.563 (1.715) 0.701 (1.721)
Constant
-0.017 (0.031)
Total credit obtained
-0.021 (0.038)
Formal credit
-0.019 (0.041)
Informal credit
Formal (VBSP)
-0.401** (0.208)
Formal (VBARD)
-0.048 (0.051)
Formal (Other)
0.047 (0.150)
-0.012 (0.031)
Credit (Agriculture)
-0.077 (0.079)
Credit (Land/Asset)
0.113*
(0.064)
Credit (Cons.)
-0.055 (0.051)
Credit (Other)
-0.012 (0.048)
Formal (Agriculture)
-0.068 (0.115)
Formal (Land/Asset)
-0.084 (0.123)
Formal (Cons.)
0.019 (0.056)
Formal (Other)
-0.013
(0.045)
Informal (Agriculture)
-0.111 (0.300)
Informal (Land/Asset)
0.305 (0.207)
Informal (Cons.)
-0.251 (1.091)
Informal (Other)
Wealth quintile (change) -1.577*** (0.547) -2.231*** (0.502) -3.121*** (0.784) -1.620*** (0.553) -1.406** (0.563)
HH_work_prop (change) -7.129** (3.143) -12.015*** (3.72) -22.557*** (5.72) -7.924** (3.143) -7.710** (3.183)
0.632* (0.344) 0.490 (0.308) 1.362** (0.660) 0.653* (0.345) 0.650* (0.345)
HHsize (change)
-0.929 (0.639) -1.103* (0.607) -3.683*** (1.072) -0.991 (0.637) -1.026 (0.638)
Shock: Natural
-0.807 (0.915) -1.186 (0.897) -0.906 (1.406) -0.761 (0.911) -1.025 (0.936)
Shock: Economic
-0.792 (1.013) -0.150 (0.983) -0.112 (1.558) -0.629 (1.011) -0.980 (1.021)
Shock: Idiosyncratic
0.301 (0.732) 0.174 (0.688) -1.319 (1.209) 0.322 (0.726) 0.464 (0.743)
Recovered from shock
Yes
Yes
Yes
Yes
Province Dummies
Yes
297
231
297
297
n
177
Note: As for Table 5.1a.
Overall, we find that the amount of credit a household has in 2006 has a positive
effect on changes in income between 2006 and 2008, even when controlling for the
factors that determine access to credit in the first place. We find strong evidence to
suggest that the effect of credit on outcomes is linked to the purpose of the credit
obtained, particularly where credit is accessed through formal sources. We find that,
for the most part, formal credit induces positive income effects while informal credit
has no effect on income. However, informal credit remains important for the incomes
of the poor. As expected, credit obtained for agricultural investments through formal
sources has a positive effect on agricultural income and credit obtained for ‘other’
purposes has a positive effect on non-farm, non-wage income. Contrary to what might
be expected, credit accessed through the two main sources of formal credit in
Vietnam, the VBSP and VBARD, has a limited effect on income levels. Instead,
credit accessed through other formal outlets such as private commercial banks, local
authorities, etc, are associated with income improvements.
Our analysis also uncovers some trade-offs in terms of the way in which funds
accessed are invested. Credit obtained for investment in land and other assets has a
negative effect on agricultural income while credit obtained for ‘other’ purposes has a
negative effect on non-farm non-wage income. This suggests that in accessing credit
for a particular purpose, households make a decision with regard to future income19
earning activities, and move away from one type of income earning activity toward
another. We also find that the way in which credit is accessed and used is very
different for poor compared with non-poor households. Both formal and informal
sources of credit are important for the agricultural incomes of the poor with both
yielding positive effects. However, access to credit has practically no impact on either
the rental income or non-farm, non-wage income of the poor.
Diversification
We now consider the impact that access to credit has on diversification of
employment within households. Diversification is often used as a means of spreading
risk but has the disadvantage of preventing households from becoming specialized in
any one activity. Specialization has the potential to yield economies of scale in
production activities leading to increased incomes and helping to alleviate poverty.
Access to credit is one way that households can invest in specialization. We measure
diversification as the number of different income sources earned by household
members and as with the previous models, measure the impact of credit on changes in
diversification between 2006 and 2008. The results are presented in Table 6a for all
households and 6b for poor households.
On the basis of our first measure, Table 6a reveals that credit reduces the extent of
diversification of income within households. We find that this is driven by credit
accessed for ‘other’ purposes through formal means. In particular, the VBSP and
other sources of formal credit play an important role. This may suggest that
households with access to credit use the opportunity to specialize in certain incomegenerating activities. This helps to explain the trade-off effects of accessing credit for
different purposes observed in the income analysis presented in Tables 5.1 to 5.4.
20
Table 6a: Effect of credit on income diversification – number of different income sources
earned by household members (all households)
Constant
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Poor (2006)
Wealth quintile (change)
HH_work_prop (change)
HHsize (change)
Shock: Natural
Shock: Economic
Shock: Idiosyncratic
Recovered from shock
Province Dummies
n
Note: As for Table 5.1a.
(1)
(2)
(3)
(4)
(5)
0.369 (0.439) -0.053 (0.271) -0.216 (0.363) 0.381 (0.441) 0.390 (0.447)
-0.002** (0.001
-0.002** (0.001)
0.004 (0.006)
-0.112*** (0.039)
-0.002 (0.002)
-0.003 (0.003)
-0.001 (0.003)
-0.003 (0.002)
-0.007 (0.009)
-0.002** (0.001)
-0.002 (0.004)
-0.004 (0.003)
-0.006 (0.018)
-0.002** (0.001)
-0.003 (0.007)
0.003 (0.007)
-0.020 (0.035)
-0.006 (0.011)
0.211 (0.159) 0.198 (0.184) -0.061 (0.285) 0.204 (0.160) 0.207 (0.161)
0.084 (0.059) 0.088 (0.064) 0.168** (0.070) 0.087 (0.059) 0.086 (0.060)
2.771*** (0.365) 3.166*** (0.404) 3.166*** (0.479) 2.772*** (0.364) 2.775*** (0.366)
0.821*** (0.063) 0.830*** (0.068) 0.764*** (0.086) 0.820*** (0.063) 0.823*** (0.063)
0.255* (0.137) 0.394*** (0.153) 0.331* (0.189) 0.252* (0.137) 0.259* (0.138)
0.110 (0.210) 0.109 (0.210)
0.106 (0.210) 0.154 (0.231)
0.082(0.266)
-0.333* (0.199) -0.393* (0.225) -0.569** (0.271) -0.334* (0.199) -0.340* (0.200)
0.014 (0.152) -0.024 (0.169) -0.090 (0.206) 0.007 (0.153) 0.008 (0.154)
Yes
Yes
Yes
Yes
Yes
1,220
859
1,220
1,220
714
Once disaggregated by poor and non-poor households (Table 6b), we find that credit
plays a very different role for poor households. Informal credit increases the level of
income diversification of households. The positive effect of informal credit for
agricultural purposes suggests that poor households may use these borrowings to
relieve household labor from agricultural production allowing it to focus on other
types of income generating activities. This result is consistent with Banerjee and
Duflo (2007) who find that poor households will often choose to diversify their
income sources rather than invest in specialization in order to spread risk, despite the
fact that specialization may yield higher returns due to economies of scale. We also
find however, that credit obtained from the VBSP has a large and significant negative
effect on income diversification suggesting that this is an important source of credit
for poor households wishing to specialize.
21
Table 6b: Effect of credit on income diversification – number of different income sources
earned by household members (poor households)
Constant
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Wealth quintile (change)
HH_work_prop (change)
HHsize (change)
Shock: Natural
Shock: Economic
Shock: Idiosyncratic
Recovered from shock
Province Dummies
n
Note: As for Table 5.1a.
(1)
0.047 (0.741)
0.014 (0.013)
(2)
-0.093 (0.787)
(3)
-0.285 (1.089)
(4)
0.224 (0.750)
(5)
0.130 (0.743)
0.008 (0.020)
0.046** (0.022)
-0.309*** (0.091)
0.018 (0.023)
0.069 (0.067)
0.011 (0.013)
0.042 (0.035)
-0.023 (0.028)
-0.018 (0.022)
-0.251 (0.238) -0.099 (0.263)
1.922 (1.370) 3.521* (1.947)
0.991*** (0.162)
0.993*** (0.150)
0.297 (0.279) 0.684** (0.319)
0.677* (0.399) 0.760 (0.471)
-0.281 (0.442) -0.471 (0.517)
0.406 (0.31)
0.324 (0.319)
Yes
Yes
297
231
-0.001 (0.021)
0.062 (0.050)
0.036 (0.053)
0.009 (0.024)
0.037** (0.019)
0.195 (0.130)
0.008 (0.089)
-0.616 (0.471)
-0.393 (0.338) -0.330 (0.242) -0.282 (0.243)
6.314*** (2.354) 2.114 (1.375) 1.818 (1.373)
1.013***
1.011*** (0.151) 0.979*** (0.149)
(0.301)
0.273 (0.498) 0.364 (0.279) 0.315 (0.276)
0.704 (0.638) 0.711* (0.399) 0.589 (0.404)
-0.581 (0.724) -0.246 (0.443) -0.258 (0.442)
-0.287 (0.551) 0.295 (0.318) 0.374 (0.321)
Yes
Yes
Yes
297
297
177
Investment
We now turn our attention to the investment behavior of households in the aftermath
of obtaining credit for various purposes. The results for the effect of credit on
investments made in land between 2006 and 2008 are presented in Table 7a for all
households and 7b for poor households.
As revealed in Table 7a, while the source of credit on aggregate does not seem to
matter for investment once credit is disaggregated by purpose we find the unexpected
result that credit for consumption purposes has a positive and significant effect on
investment. This only holds for credit accessed for consumption purposes through the
formal sector. This result is robust to controlling for selection bias associated with
households who have access (or choose to access) formal credit. It may be the case
that households who borrow for consumption purposes do so to free up other
household resources that can be used for investment purposes. In this way
consumption loans indirectly act as investment loans.
22
Table 7a: Effect of credit on investment in land (all households)
(1)
(2)
0.438 (5.053) -0.461 (3.329)
Constant
0.003 (0.013)
Total credit obtained
0.0001 (0.014)
Formal credit
-0.024 (0.078)
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
-2.213 (1.827) -1.581 (2.259)
Poor (2006)
Wealth quintile (change) 0.818 (0.684) 0.835 (0.788)
HH_work_prop (change) 7.706* (4.195) 8.817* (0.788)
0.208 (0.724) 0.199 (0.829)
HHsize (change)
1.807 (1.578) 1.823 (1.882)
Shock: Natural
5.682** (2.422) 6.181** (2.834)
Shock: Economic
-1.691 (2.296) -1.448 (2.756)
Shock: Idiosyncratic
-1.337 (1.757) -1.789 (2.076)
Recovered from shock
Yes
Yes
Province Dummies
1,220
859
n
Note: As for Table 5.1a.
(3)
5.566 (5.455)
(4)
-2.595 (5.010)
(5)
-2.663 (5.052)
-0.526 (0.563)
-0.018 (0.030)
0.008 (0.044)
0.056* (0.032)
-0.039 (0.028)
0.448*** (0.107)
0.008 (0.014)
3.832 (4.258)
0.926 (1.019)
12.081* (7.097)
0.139 (1.238)
3.771 (2.722)
7.846** (3.834)
-0.726 (3.928)
-2/262 (3.015)
Yes
714
-1.558 (1.809)
0.711 (0.677)
8.064** (4.139)
0.176 (0.715)
1.343 (1.564)
5.111** (2.393)
-1.682 (2.268)
-1.100 (1.737)
Yes
1,220
0.058 (0.045)
-0.033 (0.035)
1.072*** (0.206)
0.003 (0.014)
0.052 (0.082)
0.002 (0.076)
-0.397 (0.395)
-0.031 (0.119)
-1.504 (1.812)
0.684 (0.676)
7.246* (4.139)
0.124 (0.712)
1.592 (1.566)
5.724** (2.384)
-1.753(2.265)
-0.953 (1.741)
Yes
1,220
For poor households (presented in Table 7b) we find that credit obtained in 2006, and
in particular, formal credit has a positive effect on investments made in land between
2006 and 2008. Loans obtained through the VBARD are particularly effective. Once
we disaggregate by source of credit we find that loans obtained for agricultural
purposes are the source of this effect.
23
Table 7b: Effect of credit on investment in land (poor households)
(1)
-1.441 (4.829)
Constant
0.169** (0.087)
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Wealth quintile (change) 0.552 (1.547)
HH_work_prop (change) -0.254 (8.908)
0.048 (0.978)
HHsize (change)
0.682 (1.814)
Shock: Natural
2.789 (2.598)
Shock: Economic
0.480 (2.878)
Shock: Idiosyncratic
2.075 (2.075)
Recovered from shock
Yes
Province Dummies
297
n
Note: As for Table 5.1a.
(2)
(3)
(4)
-3.641 (5.439) -1.911 (10.369) -0.839 (4.892)
(5)
-3.616 (4.805)
0.396*** (0.143)
-0.003 (0.159)
0.171 (0.874)
0.482** (0.221)
-0.944 (0.640)
0.180** (0.088)
-0.049 (0.226)
-0.163 (0.182)
-0.011 (0.146)
0.672 (1.867) 0.250 (3.224)
3.895 (13.624) 13.403 (22.330)
-0.278 (1.151) -2.782 (2.891)
0.499 (2.282) 3.357 (4.793)
1.742 (3.321) 5.638 (6.133)
1.341 (3.733) 5.497 (6.976)
3.856 (2.586) 10.042* (5.297)
Yes
Yes
231
177
0.201 (1.572)
0.261 (8.942)
0.315 (0.987)
0.843 (1.820)
3.241 (2.601)
0.620 (2.892)
1.857 (2.070)
Yes
297
0.514*** (0.135)
0.140 (0.322)
0.152 (0.342)
0.063 (0.162)
-0.031 (0.124)
0.032 (0.839)
-0.348 (0.575)
-0.423 (1.591)
0.091 (1.565)
2.154 (8.858)
0.372 (0.967)
1.032 (1.782)
3.421 (2.609)
1.622 (2.857)
1.807 (2.074)
Yes
297
Productivity
Finally, we analyze the effect of credit on productivity. We consider two measures of
productivity. First, we consider the productivity of labor employed in agriculture
measured as the change in agricultural income as a proportion of household members
working in agriculture between 2006 and 2008. Second, we focus on rice production
and measure the change in productivity as the change rice yields (in kilograms) as a
proportion of the total land area farmed for rice production (in square meters).
The results for labor productivity for all households are presented in Table 8.1a and
for poor household in Table 8.1b. The total amount of credit obtained in 2006 has a
positive and significant effect on the change in the productivity of labor between 2006
and 2008. This is driven by credit accessed through the formal sector and in particular
through financial institutions other than the VBSP and the VBARD. Once we
disaggregate credit by source we find that credit accessed for the purpose of investing
in agriculture has a significant and positive effect, from both formal and informal
sources. We also find that credit accessed for ‘other’ purposes through the formal
sector also leads to increases in labor productivity. This can be explained by the fact
that investments resulting from this type of credit may be used to diversify household
income sources potentially leading to some household members moving away from
agricultural production. This will lead to fewer household members working on the
24
farm leading to an overall improvement in productivity using our measure. In
contrast, we find that credit accessed for investments in land and other assets has a
negative effect on agricultural productivity. This may be due to the fact that the types
of investments associated with this form of credit may move households away from
agricultural production toward income generated from the rental of these assets, as is
suggested from our earlier findings on rental income (Table 5.3a).
Table 8.1a: Effect of credit on agricultural productivity (agricultural income/number of
household members working in agriculture) (all households)
(1)
(2)
(3)
(4)
(5)
2.807
(1.807)
5.406**
(2.705)
4.158
(2.710)
6.042**
(2.727)
Constant
0.780 (3.034)
0.029*** (0.007)
Total credit obtained
0.028*** (0.007)
Formal credit
0.068 (0.052)
Informal credit
Formal (VBSP)
0.141 (0.290)
Formal (VBARD)
0.001 (0.015)
Formal (Other)
0.072*** (0.021)
0.056*** (0.018)
Credit (Agriculture)
-0.045*** (0.016)
Credit (Land/Asset)
-0.079 (0.062)
Credit (Cons.)
0.033*** (0.008)
Credit (Other)
0.102*** (0.025)
Formal (Agriculture)
-0.045**
(0.019)
Formal (Land/Asset)
-0.026 (0.137)
Formal (Cons.)
0.033*** (0.008)
Formal (Other)
0.082* (0.046)
Informal (Agriculture)
0.032 (0.053)
Informal (Land/Asset)
-0.174
(0.225)
Informal (Cons.)
0.087 (0.101)
Informal (Other)
-1.483 (1.029) -0.792 (1.157) -3.064 (2.168) -1.525 (1.016) -1.145 (1.009)
Poor (2006)
Wealth quintile (change) 0.006 (0.415) -0.315 (0.448) -0.276 (0.560) -0.041 (0.414) -0.044 (0.423)
HH_work_prop (change) -6.684** (2.811) -8.273*** (3.058) -9.867** (4.256) -6.640** (2.778) -6.485** (2.775)
-0.533 (0.401) -0.618 (0.433) -0.022 (0.640) -0.593 (0.398) -0.557 (0.396)
HHsize (change)
0.009 (0.883) 0.032 (0.985) 0.010 (1.397) -0.145 (0.876) -0.318 (0.873)
Shock: Natural
-0.024 (1.361) -0.518 (1.491) -0.638 (1.981) -0.0001 (1.348) -0.148 (1.339)
Shock: Economic
0.429 (1.327) 0.215 (1.500) 0.386 (2.056) 0.270 (1.315) 0.226 (1.308)
Shock: Idiosyncratic
-0.322 (0.982) -1.067 (1.085) -1.957 (1.517) -0.441 (0.971) -0.241 (0.968)
Recovered from shock
Yes
Yes
Yes
Yes
Province Dummies
Yes
1,116
784
1, 116
1, 116
n
654
Note: As for Table 5.1a.
When we consider poor households in isolation in Table 8.1b we find that credit
accessed for agricultural investments through formal sources has a clear positive
effect on agricultural productivity. There does not appear to be any statistically
significant difference in the source of formal credit once the usual controls for
selection bias are included.
25
Table 8.1b: Effect of credit on agricultural productivity (agricultural income/number of
household members working in agriculture) (poor households)
(1)
0.394 (1.899)
Constant
0.059* (0.036)
Total credit obtained
Formal credit
Informal credit
Formal (VBSP)
Formal (VBARD)
Formal (Other)
Informal credit
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
Wealth quintile (change) 0.101 (0.669)
HH_work_prop (change) -1.388 (4.163)
-0.331 (0.388)
HHsize (change)
0.370 (0.734)
Shock: Natural
0.597 (1.049)
Shock: Economic
1.407 (1.244)
Shock: Idiosyncratic
0.575 (0.815)
Recovered from shock
Yes
Province Dummies
279
n
Note: As for Table 5.1a.
(2)
-0.089 (2.072)
(3)
-0.402 (3.860)
(4)
0.055 (1.910)
(5)
-0.293 (1.901)
0.117** (0.055)
0.014 (0.059)
0.047 (0.319)
0.135 (0.085)
-0.216 (0.232)
0.083** (0.035)
-0.052 (0.088)
0.071 (0.074)
-0.026 (0.060)
-0.059 (0.752) 0.021 (1.233) -0.032 (0.681)
0.814 (5.379) 10.347 (9.751) -2.420 (4.160)
-0.228 (0.434) -0.527 (1.129) -0.240 (0.387)
0.606 (0.875) 2.646 (1.889) 0.395 (0.730)
-0.199 (1.254) 0.144 (2.266) 0.753 (1.042)
1.305 (1.512) 3.094 (2.994) 1.593 (1.237)
1.024 (0.970) 3.273* (1.957) 0.557 (0.809)
Yes
Yes
Yes
220
279
169
0.170*** (0.055)
0.031 (0.129)
0.129 (0.162)
-0.069 (0.065)
0.024 (0.049)
-0.070 (0.034)
0.131 (0.234)
0.336 (1.176)
-0.261 (0.700)
-2.094 (4.191)
-0.188 (0.382)
0.454 (0.725)
0.750 (1.049)
1.791 (1.228)
0.504 (0.815)
Yes
279
In Tables 8.2a and 8.2b we focus on rice production and analyze the effect of credit
on changes in the productivity of rice production measured as the change rice yields
(in kilograms) as a proportion of total land area farmed for rice production. In Table
8.2a we find that credit on aggregate does not appear to have a significant effect on
rice yields. Once we disaggregate by the purpose of the credit obtained we find that
credit accessed for the purpose of investing in agriculture from formal sources has a
positive and significant effect on the change in the productivity of rice producers
between 2006 and 2008. For poor households, the effect of credit on rice productivity
is even stronger and is evident for both formal and informal sources of credit (see
Table 8.2b) although the effect of informal credit on productivity is of a greater
magnitude than the effect of formal credit. Our results also suggest that, at least in the
case of rice production, informal credit markets, offering loans for investment in
agriculture, are effective in increasing productivity.
26
Table 8.2a: Effect of credit on rice productivity (rice yields/total rice area farmed) (all
households)
(1)
(2)
(3)
-0.332 (0.921) -0.037 (0.52) 0.006 (0.110)
Constant
0.001 (0.002)
Total credit obtained
0.001 (0.002)
Formal credit
0.007 (0.020)
Informal credit
Formal (VBSP)
0.006 (0.011)
Formal (VBARD)
0.0002 (0.001)
Formal (Other)
-0.0002 (0.001)
Informal credit
Credit (Agriculture)
Credit (Land/Asset)
Credit (Cons.)
Credit (Other)
Formal (Agriculture)
Formal (Land/Asset)
Formal (Cons.)
Formal (Other)
Informal (Agriculture)
Informal (Land/Asset)
Informal (Cons.)
Informal (Other)
-0.785** (0.329) -0.679** (0.346) -0.031 (0.086)
Poor (2006)
Wealth quintile (change) -0.149 (0.157) -0.121 (0.155) -0.005 (0.026)
HH_work_prop (change) -0.319 (0.887) -0.392 (0.882) 0.016 (0.162)
-0.284** (0.125) -0.301** (0.123) 0.003 (0.024)
HHsize (change)
0.136 (0.288) 0.288 (0.291) 0.049 (0.057)
Shock: Natural
0.383 (0.436) 0.290 (0.439) 0.020 (0.078)
Shock: Economic
0.364 (0.444) 0.233 (0.447) -0.032 (0.084)
Shock: Idiosyncratic
0.124 (0.328) -0.019 (0.333) -0.108* (0.063)
Recovered from shock
Yes
Yes
Province Dummies
Yes
835
576
n
508
Note: As for Table 5.1a.
27
(4)
-0.445 (0.926)
(5)
-0.556 (0.952)
0.013** (0.007)
-0.009 (0.008)
0.008 (0.030)
-0.0001 (0.003)
0.017** (0.008)
-0.004 (0.010)
0.007 (0.060)
-0.0005 (0.003)
0.013 (0.023)
0.005 (0.032)
0.018 (0.105)
-0.001 (0.035)
-0.768** (0.329) -0.741** (0.331)
-0.154 (0.157) -0.168 (0.161)
-0.372 (0.885) -0.378 (0.893)
-0.289** (0.125) -0.289** (0.126)
0.126 (0.288) 0.114 (0.289)
0.333 (0.437) 0.325 (0.347)
0.341 (0.443) 0.351 (0.444)
0.123 (0.327) 0.153 (0.330)
Yes
Yes
835
835
Table 8.2b: Effect of credit on rice productivity (rice yields/total rice area farmed) (poor
households)
(1)
(2)
(3)
(4)
(5)
-2.657 (2.343) -1.784 (2.169) 0.561 (0.524) -2.972 (2.311) -3.016 (2.213)
Constant
0.185** (0.075)
Total credit obtained
0.171** (0.075)
Formal credit
0.781*** (0.198)
Informal credit
Formal (VBSP)
-0.069 (0.049)
Formal (VBARD)
-0.018 (0.013)
Formal (Other)
-0.141*** (0.032)
0.248*** (0.068)
Credit (Agriculture)
-0.179* (0.109)
Credit (Land/Asset)
-0.015 (0.115)
Credit (Cons.)
-0.221** (0.097)
Credit (Other)
0.268*** (0.078)
Formal (Agriculture)
-0.017 (0.151)
Formal (Land/Asset)
0.332 (0.306)
Formal (Cons.)
-0.200* (0.116)
Formal (Other)
0.913*** (0.208)
Informal (Agriculture)
-0.089 (0.419)
Informal (Land/Asset)
-0.260 (0.331)
Informal (Cons.)
-0.094 (1.264)
Informal (Other)
Wealth quintile (change) -1.090 (1.011) -0.610 (1.014) -0.017 (0.226) -2.170** (0.994) -2.157** (0.991)
HH_work_prop (change) 0.702 (4.930) 1.211 (5.904) 0.427 (1.433) 1.705 (4.821) 3.551 (4.897)
-1.451*** (0.495) -1.581*** (0.465) 0.158 (0.146) -0.758 (0.501) -0.881* (0.475)
HHsize (change)
-0.057 (0.899) -0.113 (0.952) 0.185 (0.250) 0.478 (0.874) 0.387 (0.852)
Shock: Natural
1.257 (1.466) 0.855 (1.463) 0.617* (0.372) 2.154 (1.397) 2.549* (1.414)
Shock: Economic
0.275 (1.500) 0.202 (1.575) 0.057 (0.345) 0.956 (1.450) 0.039 (1.448)
Shock: Idiosyncratic
0.546 (1.047) 0.608 (1.078) -0.535** (0.270) 0.372 (1.003) 0.568 (1.007)
Recovered from shock
Yes
Yes
Yes
Yes
Province Dummies
Yes
162
217
217
217
n
133
Note: As for Table 5.1a.
4. Summary and Policy Implications
In this paper we construct a profile of households using credit from different sources
for a variety of purposes. We consider: 1) the factors determining access to credit; 2)
the factors determining how loans are used by households; and 3) the effect of various
forms of credit on outcomes.
We find that access to credit in rural Vietnam is very high but the sources of credit
vary considerably across different household groups. We find that access to formal
credit is more associated with wealthier more educated households while the opposite
is the case for informal credit. Savings act as a substitute for credit rather than a
complement as found in other studies (Baland et al., 2007). In contrast, ownership of
land is positively correlated with access to formal credit suggesting that land plays an
important role in serving as collateral for loans with formal financial institutions and
may be an important access route not available to poorer, landless households. We
also find that poor households are much more likely to have loans with the VBSP and
are much less likely to have loans with the VBARD. Similarly, education is an
important factor for accessing VBARD loans but less so for loans from the VBSP.
These findings have important implications for understanding the effectiveness of
credit on poverty alleviation. In particular, since we find that the effectiveness of
28
credit on welfare outcomes varies considerably depending on the source of credit
(formal/informal and VBSP/VBARD), understanding the characteristics of the
households that have access (or choose to access) different forms of credit is an
important step in understanding why some forms of credit work and others do not.
The use of the loans obtained also varies by household characteristics. High income
households and more educated households are more likely to use credit to invest in
land or assets or other non-farm activities such as enterprise development. In contrast,
poorer households are more likely to hold credit for agricultural investments. Credit
for consumption is important for older households but larger households with a
greater proportion of working household members are much less likely to borrow for
consumption. Households who suffer an idiosyncratic shock also rely on credit to
smooth consumption in times of economic hardship. These results highlight the very
different roles that credit plays in the lives of rural Vietnamese households and
highlights the need to focus on the differential impact of credit on different groups of
households. In particular, it is important to understand the extent to which the source
of credit obtained, and how those funds are used, impacts on outcomes for different
household groups.
The core of our empirical investigation explores how access to credit affects
outcomes, focusing on the source of credit obtained, how the funds are used and, in
particular, on the differences in the effectiveness of credit for poor and non-poor
households. In what follows, we summarize our key findings.
In the income analysis presented in Tables 5.1 to 5.4 we find that the amount of credit
a household has in 2006 has a positive effect on changes in income between 2006 and
2008, even when controlling for the factors that determine access to credit in the first
place. We find strong evidence to suggest that the effect of credit on outcomes is
linked to the purpose of the credit obtained, particularly where credit is accessed
through formal sources. We find that, for the most part, formal credit induces positive
income effects while informal credit has no effect on income. However, informal
credit remains important for the incomes of the poor. As expected, credit obtained for
agricultural investments through formal sources has a positive effect on agricultural
income and credit obtained for ‘other’ purposes has a positive effect on non-farm,
non-wage income. Credit obtained for the purpose of investing in land and other
assets from informal sources yields positive returns on rental income for non-poor
households. Formal credit, however, is not found to have any effect. This may suggest
that formal credit for this particular purpose may be rationed. Contrary to what might
be expected, credit accessed through the two main sources of formal credit in
Vietnam, the VBSP and the VBARD, has a limited effect on income levels. Instead,
credit accessed through other formal outlets such as private commercial banks, local
authorities, etc, are associated with income improvements.
From a policy point of view these results suggest that access to formal credit is
important for the incomes of Vietnamese households, in particular, for agricultural
incomes and non-farm, non-wage incomes and so improving access to commercial
financial institutions for these purposes may help in improving the incomes of the
poor who still rely on informal credit markets which appear less effective. Our
findings also suggest that further investigations into the workings of the formal credit
market for investment in rental assets are required since, on the basis of our analysis,
29
they appear to be non-existent. Further investigation is also required into the
effectiveness of formal credit obtained from various sources. Understanding why
commercial institutions are more effective than the VBARD and the VBSP will be
important for designing future government policies in relation to rural credit markets.
Our income analysis also reveals a number of trade-offs between the use of funds and
different income earning activities suggesting that in accessing credit for a specified
purpose, households are more likely to specialize in that activity. This leads to
increases in income earned from their chosen specialist activity and a reduction in
income earned from other activities. In support of this conclusion we find that access
to formal credit reduces the extent of income diversification of households, inducing
them to become more specialized (see Tables 6a and 6b). In contrast, we find that for
poor households, access to credit through informal sources increases the level of
income diversification of households suggesting that poor households prefer to spread
risk across different income earning activities rather than specializing. This result is
consistent with other findings in the literature (Banerjee and Duflo, 2007). The fact
that poor households do not specialize means that they do not benefit from the
economies of scale that would allow them to move from small scale production units
to large-scale viable enterprises. This may act as a barrier to poverty alleviation. We
do find evidence however, that credit obtained from the VBSP has a negative and
significant effect on diversification suggesting that these funds are used for
specialization. For poor households we find that these funds are used to specialize in
agriculture. This may be an important vehicle for poverty alleviation and our findings
suggest that the VBSP is successful in achieving this goal. It should be noted,
however, that particularly for poor household, the need for income diversification as a
means of spreading risk may also be an important welfare outcome. Further
investigation will be required into understanding the conflicting needs of poor
households to diversify income to spread risk or specialize to improve profitability.
The level of credit obtained is found to have a very limited effect on the investment
behavior of households (see Tables 7a and 7b). There is some evidence to suggest that
formal credit, accessed for consumption purposes, has a positive effect. This
contradicts much of the literature (see for example, Hung (2005)) which suggests that
consumption loans can stagnate economic development. In contrast, our results
suggest that consumption loans (accessed through the formal sector) may in fact have
investment enhancing effects. We explain this by the fact that households who borrow
for consumption purposes may do so to free up other household resources that can be
used for investment purposes. In this way consumption loans indirectly act as
investment loans. Further investigation will be required in order to understand why
credit obtained for investment purposes does not lead to increases in observed
investment levels. In contrast, for poor households we find that credit obtained in
2006, and in particular, formal credit has a positive effect on investments made in
land between 2006 and 2008. Loans obtained through the VBARD are particularly
effective. Once we disaggregate by source of credit we find that loans obtained for
agricultural purposes are the source of this effect.
The final part of our empirical analysis explores the relationship between credit and
productivity. We find that credit has very strong and robust effects on productivity.
For rice producers, particularly poor rice producers, productivity gains are realized
whether credit is accessed through the formal or informal sector and the positive
30
effect appears to be even stronger for poor households. For agricultural productivity in
general, however, only loans accessed through the formal sector have a positive
effect. Consistent with our earlier findings we find that loans accessed through formal
commercial financial institutions other than the VBSP and the VBARD appear to be
the most important sources of formal credit.
Overall, our results provide strong evidence that credit is an effective instrument for
improving welfare outcomes, and by consequence, for alleviating poverty. However,
the source of credit (formal or informal) and the purpose of the credit are important
factors to understand in designing effective policy. This is particularly the case given
that the effectiveness of the source and purpose of credit in improving outcomes
differs for poor and non-poor households. We find that formal credit is much more
effective than informal credit. One possible explanation for this is that the monitoring
of the use of credit and contract enforcement, in general, may be more difficult when
credit is accessed informally (Banerjee and Duflo, 2004). Households accessing
informal credit may, therefore, be more likely to use credit for purposes not linked
with the original stated purpose on the loans thereby diluting the effect of credit. One
finding of particular note is that we do not find evidence that credit accessed through
the VBSP has a significant effect on outcomes, even for poor households. The only
exception is some evidence that VBSP loans help households to specialize production.
This does not extend to improvements in welfare outcomes, however. While it could
be argued that we cannot fully control for potential differences in the unobserved
characteristics of households that access commercial financial institutions that may
also influence outcomes, we can conclude that credit accessed through formal
commercial financial institutions is certainly associated with improved outcomes on
most of the metrics considered in this paper, for both poor and non-poor households.
There are many reasons why this may be the case. The most likely reason is that
commercial financial institutions require more rigorous conditions on households in
terms of collateral and ability to pay criteria before extending credit to these
households. Similarly, it could also be due to a self-selection effect whereby
households that are more likely to experience improved outcomes are also more likely
to access these institutions. Controlling for these factors, however, we find many
correlations between access to formal commercial financial institutions and
improvements in outcomes while we find no such correlations between access to
VBSP credit and improvements in outcomes. While causal relationship cannot be
definitively established the results from this research suggest that finding ways of
improving access to formal commercial institutions for poor households may help in
poverty alleviation. Whether it is in the process of preparing households to be eligible
to access credit from commercial institutions (by, for example, improving education
outcomes, income security, building up collateral, etc.) that ultimately leads to
improvements in outcomes or whether it is the funds themselves that matter remains
unclear.
As highlighted in the introduction, this paper does not address the extent to which
extending credit is the best approach to alleviating poverty given that other policy
instruments are not considered. Furthermore, it does not address the question of
whether rural households are over-indebted. Future research analyzing the operation
of the complete financial market in rural areas (including credit, savings and
insurance) is therefore a natural extension of this work and an important theme for
future research.
31
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32
Appendix
Technical details of econometric methods15
The Bivariate Probit Model
A standard probit model is used in cases where we have a binary dependent variable.
To derive this model we assume that there exists an underlying latent model with a
continuous, but unobservable dependent variable. For example, if we interpret the
latent variable as an index of utility preferences relating to households desire to access
credit we can assume that this utility index can be explained by the same set of
characteristics as the observed actions of the individual, that is the observed outcome
of whether the household accesses credit or not.
The model can be described as follows:
y*  x' β  e
y 1
y0
where y * is unobserved and therefore latent
if y*  0 i.e. utility is positive
if y*  0 i.e. utility is zero or negative
where y * is the underlying utility associated with accessing credit, y is the observed
binary outcome, that is whether the household accessed credit or not, x is a vector of
explanatory variables, β is a vector of parameters that we wish to estimate and e is a
statistical noise or disturbance term. Assuming that the statistical noise term follows a
standard normal distribution ( e ~ N 0,1 ) we can construct a log-likelihood equation
for this model that can be estimated using maximum likelihood estimation. The loglikelihood equation takes the following form:
ln L   ln 1  Φx' β    lnΦx' β 
y 0
y 1
where Φ .  is the cumulative distribution function for the standard normal
distribution and Φ  x'β  represents the probability of observing y  1 (for example, a
households that has access to credit).
A natural extension of the probit model is to allow more than one equation with
correlated disturbances. This model would apply to situations where we believe two
separate outcomes are determined simultaneously. In this paper, this may apply to the
decision to access credit from either formal or informal sources.
A general specification for the two equation model would be:
y1*  x ' β1  e1 , y1  1 if y1*  0 and 0 otherwise
y2 *  x ' β2  e2 , y2  1 if y2*  0 and 0 otherwise
15
A subscript representing each observational unit in the sample is suppressed for ease of exposition.
Characters in bold font represent vectors while those in standard font represent scalars.
33
where y1* is the underlying utility associated with accessing formal credit, y1 is the
observed binary outcome, that is whether the household accessed formal credit or not,
y 2* is the underlying utility associated with accessing informal credit, y 2 is the
observed binary outcome, that is whether the household accessed informal credit or
not, x is a vector of explanatory variables, β1 is a vector of parameters associated
with access to formal credit, β2 is a vector of parameters associated with access to
informal credit, and e1 and e2 are statistical noise or disturbance terms associated
with each decision.
For the mean of the distribution of the disturbance terms we assume that:
Ee1 | x1 ,x 2   Ee2 | x1 ,x 2   0
For the variance of the distribution of the disturbance terms we assume that:
V e1 | x1 ,x 2   V e2 | x1 ,x 2   1
We also assume that the covariance between the two error terms is given by:
Cove1 ,e2 | x1 ,x 2   
The model will collapse to two separate probit models when   0 . Where a
correlation between the two error terms exists it indicates that the unobserved effects
determining the two binary outcomes are related to each other and so consideration of
this must be made in estimating the parameters of the model. This model is also
estimated using maximum likelihood estimation procedures.
The Sample Selection Model
In cases where the sample is in some way selected or non-random using standard
regression techniques will result in biased parameter estimates. In this paper, when
analyzing both the types of credit that households access and the effects of credit on
outcomes we must control for the fact that households who access credit are selfselecting. These households may possess characteristics that are related to both the
fact that they access credit and the outcomes that we are analyzing. To control for the
bias that this introduces to parameter estimates using standard regression techniques
we use Heckman’s sample selection model. This requires a two stage estimation
procedure where in the first stage the factors determining access to credit are modeled
using a probit model. These factors are controlled for in the second stage regression
on the outcome variable of interest by including the inverse mills ratio from the first
stage probit model.
Formally, the first stage selection mechanism is given by:
y*  x' β  e ; y  1 if y*  0 and zero otherwise.
P  y  1| x     x '  
P  y  0 | x   1   x ' 
34
where each term is as for the probit model above.
The second stage regression model is given by:
z  w '   u observed only if y  1
  0   1  eu  
e 
  with correlation 
 u  ~ N   0  , 
2 


 


 eu u  

where z is the observed continuous variable (either the proportion of total credit
accessed for a particular purpose as in section 3.2 or a particular outcome variable as
in section 3.3), w is a vector of explanatory variables that must differ by at least one
variable to x for identification,  is a vector of parameters to be estimated and u is a
statistical noise or disturbance term. z is only observed for households that access
credit (i.e. y  1 ).
The model can be written as:
E  z | y  1, w, x   w '      x '  
where   x '      x '     x '   ,  .  is the standard normal density function and
 . is the standard normal cumulative distribution function. The term   x '   is a
sample selection adjustment term that adjusts for sample selection bias in the second
stage regression.
We use Heckman’s two step estimation procedure to estimate this model. In the first
stage we estimate the probit model using maximum likelihood estimation, as
described above, to obtain estimates for  and use these estimates to compute the
estimated Inverse Mills Ratios ˆ x ' ˆ   x ' ˆ  x ' ˆ for each observation in the
     
sample. In the second stage we estimate ̂ and ˆ using an ordinary least regression
of z on w and ˆ .
35
Independent Variables and Summary Statistics
Income
Poor
Age1
HHsize
HH_work_prop
Total Area Owned
Description
Deflated annual household income from all sources
(VND ‘000)
Households in the bottom quintile of the wealth
distribution constructed based on food expenditure
(deflated value of per capita food consumption in the
previous 4 weeks in VND ‘000)
Food quintile 1
Food quintile 2
Food quintile 3
Food quintile 4
Food quintile 5
Age of head of household
Size of household
Proportion of active household members in work
Total area of agricultural land owned (square metres)
Education
2006
28,184
2008
43,601
29.98
36.27
62.16
82.30
93.94
136.75
142.01
206.51
311.21
421.99
49.49
33.33
4.78
4.75
94.81
94.18
10,315
8,847
Frequency (%)
Level of education of head of households
Cannot read and write/did not attend school
36.87
48.21
Completed lower primary
21.39
8.60
Completed lower secondary
29.17
26.88
Completed Upper secondary
10.40
15.95
College/University
2.17
0.36
Formal Saving
5.53
5.30
Dummy variable =1 if household has formal saving
Informal Saving
9.19
3.58
Dummy variable =1 if household has informal saving
Home Saving
45.26
38.24
Dummy variable =1 if household has home saving
Shock: Natural
Dummy variable =1 if household suffered natural disaster
32.71
45.14
between 2002 and 2006
Shock: Economic
Dummy variable =1 if household suffered economic
1.12
14.87
shock between 2002 and 2006
Shock: Idiosyncratic Dummy variable =1 if household suffered idiosyncratic
20.31
12.48
shock between 2002 and 2006
Province Dummies Dummy variable =1 if household located in province:
Ha Tay
12.99
Lao Cai
6.50
Phu Tho
8.44
Lai Chau
8.29
Dien Bien
7.92
Nghe An
14.49
Quang Nam
8.51
Khanh Hoa
2.54
Dak Lak
10.38
Dak Nong
6.35
Lam Dong
5.00
Long An
8.59
Wealth Quintile
Wealth quintile based on principal component analysis of
the characteristics of the household dwelling place, such
as the size and value of the dwelling, energy supply,
sanitation facilities and water supply and ownership of
durable goods.
Note: Summary statistics are based on sample of 1,339 households used in the analysis.
1
The difference in the average age of the head of household may be due to the fact that in 2006
individuals were asked to state their age while in 2008 individuals were asked what year they were
born. This will not affect our results given that only age in 2006 is used in our regression analysis.
36
Outcome Variables and Summary Statistics
2006
Total income (Mean ‘000 VND)
28,694
Agricultural income (Mean ‘000 VND)
11,089
Rental income (Mean ‘000 VND)
517
Income from non-farm, non-wage activities (Mean ‘000 VND)
4,534
Income diversification: number of different sources of income
4.36
earned by household members (Mean)
Investment in land between 2006 and 2008 (Mean ‘000 VND)
2,948
Agricultural productivity: agricultural income/number of
5.15
household members working in agriculture (Mean VND)1
Rice productivity: rice yields/total rice area farmed (Mean)2
8.32
Note: Summary statistics are based on the sample of households who access credit in
households).
1
Based on a sample of 751 households.
2
Based on a sample of 576 households.
37
2008
44,835
17,375
396
7,883
4.78
8.32
4.74
2006 (855
Consumer Price Index
Month
CPI
July
100.00
August
99.85
September
99.70
October
99.24
North East
2006
July
90.09
August
89.85
September
89.31
October
89.19
North West
2006
July
98.14
August
97.86
September
97.37
October
96.37
North Central Coast
2006
July
85.52
August
85.52
September
85.19
October
84.85
South Central Coast
2006
July
96.92
August
98.88
September
97.94
October
97.72
Central Highlands
2006
July
92.43
August
92.01
September
91.67
October
91.42
Mekong River Delta
2006
July
95.14
August
95.29
September
94.88
October
94.90
2008
July
115.72
Red River Delta
August
115.76
September
117.02
North East
2008
July
104.37
August
104.97
September
106.05
North West
2008
July
114.39
August
114.77
September
116.58
North Central Coast
2008
July
102.61
August
103.11
September
104.09
South Central Coast
2008
July
116.18
August
117.55
September
117.95
Central Highlands
2008
July
111.88
August
112.41
September
113.26
Mekong River Delta
2008
July
115.67
August
115.81
September
116.35
Source: Computed based on CPI data available in the Vietnamese Household Living Standards Survey.
Region
Red River Delta
Year
2006
38